Adjusting Content Delivery Based On User Submissions

ABSTRACT

Methods, and systems, including computer programs encoded on computer-readable storage mediums, including a method for adjusting content delivery based on user submissions. The method includes analyzing user submissions comprising photographs, the analyzing comprising: for each of the user submissions: identifying a time the user submission occurred; identifying objects represented in the photograph; determining a subject matter of the user submission based at least in part on the objects; determining a geographic location associated with the subject matter of the user submission; determining clusters of the user submissions, each user submission in a particular cluster being similar to each other user submission in the particular cluster based on the times the user submissions occurred, the subject matters of the user submissions, and the geographic locations associated with the user submissions; and adjusting content delivery to members of a network based on the determination of one or more of the clusters.

BACKGROUND

This specification relates to information presentation.

The Internet provides access to a wide variety of resources. Forexample, video and/or audio files, as well as web pages for particularsubjects are accessible over the Internet. Further, online socialnetworks are another resource that can be accessed over the Internet.

Online social networks permit users to post information and communicatewith other people, such as their friends, family, and co-workers. Socialnetwork users can post, for example, information about themselves, theirfriends and events or activities about which they are interested or areotherwise aware. Given the number of social network users and the easeof posting information, e.g., through Internet-ready mobile devices,vast amounts of user submissions (e.g., posts) are posted daily onsocial networks. However, much of the information in such posts is ofinterest to only a small fraction of the social network user populationas many posts are meant for consumption by friends or family of theposter, e.g., a post from a social network user about the social networkuser's dinner plans.

Information in the posts, and the intensity of such posts, can beindicative of trending topics or newsworthy events. However, given thevast amount of information posted and the local user audiences to whichmany posts are directed or are of interest, it can be challenging todistill the posts to identify those posts that relate to important,newsworthy or otherwise interesting events or topics that appeal orlikely appeal to the general population of social network users or alarger population of Internet users, and not just to a particular groupof users of a social network.

SUMMARY

In general, one aspect of the subject matter described in thisspecification can be implemented in methods that include analyzing usersubmissions to a network, wherein each of the user submissions comprisea photograph, the analyzing comprising: for each of the usersubmissions: identifying a time the user submission occurred;identifying one or more objects represented in the photograph from theuser submission; determining a subject matter of the user submissionbased at least in part on the one or more objects identified from theuser submission; determining a geographic location associated with thesubject matter of the user submission based at least in part on contentof the user submission; determining, by one or more processors, clustersof the user submissions, wherein each user submission in a particularcluster is similar to each other user submission in the particularcluster based at least in part on the times the user submissionsoccurred, the subject matters of the user submissions, and thegeographic locations associated with the subject matter of the usersubmissions; and adjusting delivery of content to members of the networkbased on the determination of one or more of the clusters.

Other embodiments of this aspect can include corresponding systems,apparatus, and computer programs, configured to perform the actions ofthe methods, encoded on computer storage devices.

These and other embodiments can each optionally include one or more ofthe following features. The methods can also include identifying searchqueries for which responsive search results were selected thatreferenced the one or more objects and identifying one or more termsfrom the search queries as the subject matter of the user submission.

The methods can also include identifying clusters that have a number ofuser submissions that exceed a respective cluster threshold value andincreasing a delivery volume of content to the network for contenthaving a subject matter similar to that of the subject matter of theuser submissions in the clusters that exceed their respective clusterthreshold values. Each of the cluster threshold values can be based onthe subject matter of user submissions in the respective cluster. Eachof the cluster threshold values can alternatively or additionally bebased on a volume of user submissions in the respective cluster and atime period during which those user submissions occurred.

The methods can also include determining the geographic location basedat least in part on a geotag for the user submission. The methods canalso include determining the geographic location based at least in parton geographic information for the one or more objects.

The methods can also include providing content including a headline witha link linking to an aggregation of user submissions from one or more ofthe determined clusters. The user submissions can be user posts to thenetwork and the network can be an online social network.

Particular implementations of the subject matter described in thisspecification can be implemented to realize one or more of the followingadvantages. The subject matter of user photographic submissions (e.g.,social network posts that include photographs and/or videos) to anetwork and the number of such submissions with common photographicsubject matter can be used to determine important, newsworthy orotherwise interesting events or topics that likely appeal to a generalpopulation of network users. This leads to an additional layer ofinformation gain.

In general, user submissions can be an indication of user interests inthe subject matter of the submissions. Further, user photographicsubmissions are likely an even stronger indication of user interests as,beyond merely submitting textual content in a user submission, the usersperformed the additional step of taking photographs of the subjectmatter of interest and including the photographs in their submissions.Providers of content on the network (e.g., the social network providers)can leverage this strong indication of interest from user photographicsubmissions, for example, in determining the subject matter of contentto distribute across the network. Further, the strong indications ofinterest from user photographic submissions can also be used to providea better sematic understanding of search queries submitted to a searchsystem.

More particularly, these content providers can focus on the subjectmatter of the photographs and the number of photographs with related orcommon subject matter to identify important, newsworthy or otherwiseinteresting events or topics (e.g., trending topics), rather thansifting through all user submissions, including text submissions thatmay not be the strongest indications of user interests. This reduces theprocessing burdens (e.g., system processing and bandwidth requirements)on the content providers in analyzing submissions as some or all of thetext-only user submissions can be ignored or processed/analyzed at alower priority. Additionally, as the user photographic submissionslikely indicate a stronger user interest, selecting content based on theuser photographic submissions to provide or distribute across thenetwork will likely be more appealing or interesting to network users(or a broader group of network users) than content selected based onlyon text-only user submissions.

The details of one or more implementations of the subject matterdescribed in this specification are set forth in the accompanyingdrawings and the description below. Other features, aspects, andadvantages of the subject matter will become apparent from thedescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example environment in which a contentadjustment delivery system can be implemented.

FIG. 2A is a flow diagram of an example process for adjusting thedelivery of content.

FIG. 2B is an example photograph from a user submission.

FIG. 2C is an example diagram of user submission clusters.

FIG. 3 is a block diagram of a programmable processing system.

Like reference numbers and designations in the various figures indicatelike elements.

DETAILED DESCRIPTION

This description generally relates to analyzing user submissions, suchas user posts of photographs and/or videos, to a social network inreal-time or near real time to identify unusual, interesting and/orcurrent events based on the subject matter and timing of the usersubmissions, the geographic location of such subject matter, theintensity or rate of relevant user submissions or a combination thereof.The identification of such subject matter/events can be used, forexample, to determine which content (e.g., news feeds) may be ofinterest and be delivered to members of the social network.

More particularly, objects in photographs or videos in user submissionscan be analyzed to identify the subject matter of the user submissions.For example, various object-based detection algorithms can be used toidentify an object in a photograph in a user submission, and the subjectmatter of the identified object can be determined to be the subjectmatter of the user submission. The geographic location associated with auser submission/photograph can be determined through identification ofobjects (e.g., landmarks) in the photograph with known locations as wellas through, for example, photograph geotags, IP address reconciliation,and other location determination processes.

As the user submissions are analyzed, clusters of user submissions withcommon subject matter are determined. The determination of clusters ofuser submissions may indicate important, newsworthy or otherwiseinteresting events related to the subject matters of the clusters. Thedetermination of such clusters can be used to decide which contentshould be delivered to members in the social network, as describedbelow.

FIG. 1 is a block diagram of an example environment in which a contentadjustment delivery system 110 can be implemented. The exampleenvironment 100 includes a network 102, such as a local area network(LAN), a wide area network (WAN), the Internet, or a combinationthereof. The network 102 can connect websites 104, user devices 106, thesocial network system 108, the content adjustment delivery system 110and the search system 112. The example environment 100 may include manythousands of websites 104 and user devices 106.

A website 104 can be one or more resources 105 associated with a domainname and hosted by one or more servers. An example website is acollection of web pages formatted in hypertext markup language (HTML)that can contain text, images, multimedia content, and programmingelements, such as scripts. Each website 104 can be maintained by apublisher, which is an entity that controls, manages and/or owns thewebsite 104.

A resource 105 can be any data that can be provided over the network102. A resource 105 can be identified by a resource address that isassociated with the resource 105. Resources include HTML pages, wordprocessing documents, and portable document format (PDF) documents,images, photographs, video, and feed sources, to name only a few. Theresources 105 can include content, such as words, phrases, images andsounds, that may include embedded information (such as meta-informationin hyperlinks) and/or embedded instructions (such as scripts).

A user device 106 can be an electronic device that is under control of auser and is capable of requesting and receiving resources over thenetwork 102. Example user devices 106 include personal computers, mobilecommunication devices, and other devices that can send and receive dataover the network 102. A user device 106 typically includes a userapplication, such as a web browser, to facilitate the sending andreceiving of data over the network 102.

To facilitate searching of the resource 105 and websites 104, theenvironment 100 can include a search system 112 that can identify theresources 105 by crawling and indexing the resources 105 provided bypublishers on the websites 104. Data about the resources 105 can beindexed based on the resource 105 to which the data corresponds. Theindexed and, optionally, cached copies of the resources 105 can bestored in an indexed cache 114.

User devices 106 can submit search queries to the search system 112 overthe network 102. In response, the search system 112 can access theindexed cache 114 to identify resources 105 that are relevant to thesearch query. The search system 112 can identify the resources 105 inthe form of search results and can return the search results to the userdevices 106 in search results pages. A search result can be datagenerated by the search system 112 that identifies a resource 105 thatis responsive to a particular search query, and includes a link to theresource 105. An example search result can include a web page title, asnippet of text or a portion of an image extracted from the web page,and the URL of the web page.

The social network system 108 can include a system through which anonline social network can be implemented or otherwise subsist. Asdescribed above, an online social network can provide an environmentthrough which users (e.g., social network members) can interact withother users. For example, users can use user devices 106 to access thesocial network through the social network system 108 and postinformation and communicate with other users, such as their friends,family, and co-workers. In some implementations, the social networksystem 108 includes one or more websites 104. In some implementations,users can create and maintain user member web pages (e.g., resources105) on the social network system 108 to which users can, for example,post user submissions.

In some implementations, in additional to user member web pages, thesocial network system 108 can include, and provide access to, usercommunity web pages and general content web pages. For example, suchother web pages can include content such as news stories about currentevents, trending topics (e.g., most searched topics or subject matter),targeted content such as advertisements, summaries of recently posteduser submissions, and the like. In some implementations, such content isdisplayed by user devices 106 as headlines on user member web pages.

As described above, users (e.g., social network members) can use userdevices 106 to create content and submit posts to a social networkservice (e.g., the social network system 108 or another type ofuser-generated content service). For example, a social network membercan take a photograph (or video) of an event of interest to that memberand post the photograph or the photograph and a textual description ofor comment about the photograph to the social network service so thatthe photograph or photograph and comment can be viewed by other membersof the social network. In some implementations, the social networkmember can post the photograph to the member's profile web page so thatother social network members that have a relationship (e.g., arefriends) with the posting social network member can view the post byusing a user device 106 to request access to the posting social networkmember's profile page.

As used herein a user submission is an item (e.g., photograph, video,textual comment, or some combination thereof) posted or provided to thesocial network system 108 for access by one or more user devices 106therefrom. For example, a user submission can be or can be accessiblethrough a resource 105 (e.g., a social network member's profile webpage). In some implementations, user submissions can be stored (e.g., bythe social network system 108) in a user submission data store 124. Theuser submissions can be stored and indexed according to, for example,the time the user submissions were posted, the subject matter of thepost, the geographic location of the subject matter of the post, or anyother factor.

In some implementations, content provided by or accessible from thewebsites 104, the social network system 108 and/or the search system 112can be adjusted or otherwise affected by the content delivery adjustmentsystem 110 based on the user submissions. For example, with respect tothe social network system 108, the content delivery adjustment system110 can determine (or adjust) which current events should be representedby headlines displayed on member web pages based on the number orsubmission rate of user submissions concerned with those current events.By way of another example, with respect to search results provided bythe search system 112, the content delivery adjustment system 110 canprovide indications to the search system 112 of topics or subject matterof recent interest based on the number or submission rate of usersubmissions concerned with those topics or subject matter. In turn, thesearch system 112 can use the indications to provide additional contextand semantic meaning for submitted search queries to increase thelikelihood responsive search results satisfy the submitting user'sinformational needs.

As described above, user submissions that include photographs or videosoften provide a stronger indication of interest than do user submissionsthat only include textual content. Thus, in some implementations, thecontent delivery adjustment system 110 can analyze, at least in part,user submissions that include photographs in determining how to adjustcontent delivery. Content delivery adjustment and the operation of theobject identification apparatus 116, the subject matter identificationapparatus 118, the geographic location identification apparatus 120 andthe cluster determination apparatus 122 of the content deliveryadjustment system 110 are described with reference to FIGS. 2A, 2B and2C.

With reference to FIG. 2A, which is a flow diagram of an example processfor adjusting the delivery of content, the process 200 analyzes usersubmissions to a network (202). The network can be a network thatincludes a community of members that are a proper subset of members ofanother network. For example, the network can be members of a socialnetwork represented by a social network system 108 and the other networkcan be all users of the Internet.

In some implementations, the content delivery adjustment system 110 iscommunicatively coupled to the social network system 108. As such, thecontent delivery adjustment system 110 can receive the user submissionsor otherwise accesses user submissions (e.g., from the user submissiondata store 124) and can analyze the user submissions to identify groupsof user submissions with commonalities such as, for example, commonsubject matter (e.g., clusters of user submissions). For example, theuser submissions can include photographs and, as described below, thecontent delivery adjustment system 110 can determine the commonalitiesbased on the subject matter of the photographs.

The content delivery adjustment system 110 can use the identifiedclusters of user submissions to determine or adjust which content shouldbe delivered, for example, through the social network system 108 tosocial network members. For example, an identified cluster may includeten thousand user submissions, posted during a five minute window,related to a tornado in Oklahoma City, Okla.. Based on the submissionrate of user submissions in the cluster (e.g., the number of usersubmissions received over a given time period), the content deliveryadjustment system 110 can determine that a news feed about the tornadoshould be delivered to the social network members through the socialnetwork system 108.

In some implementations, the analysis of user submissions includesprocesses 202A-202D for each user submission. The process 200 identifiesa time the user submission occurred (202A). In some implementations, thecontent delivery adjustment system 110 can identify a time the usersubmission occurred based on the time the user submission is posted tothe social network system 108 or the time the photograph in the usersubmission was taken (e.g., as determined from metadata for thephotograph). For example, the content delivery adjustment system 110 canidentify a time the user submission occurred by “time stamping” the usersubmission with the time the user submission is posted to the socialnetwork system 108 based on the system clock of the server or othercomputing device in which the content delivery adjustment system 110 isimplemented. In some implementations, the content delivery adjustmentsystem 110 can access, through the social network system 108, usersubmissions from the user submission data store 124 and identify thetimes user submissions occurred from metadata associated with the usersubmissions.

The process 200 identifies one or more objects represented in thephotograph from the user submission (202B). An object can be any visibleor tangible element or thing captured or otherwise represented in aphotograph. For example, an object can be a person, a building, avehicle, or a weather event to name just a few. In some implementations,the object identification apparatus 116 can identify objects representedin the photograph included in the user submission. The objectidentification apparatus 116 can use various techniques to identifyobjects in a photograph such as scale invariant feature transform(SIFT), edge detection, interest point detection, pixel matching, andother appropriate image processing techniques. Process 202B is furtherdescribed with reference to FIG. 2B, which is an example photograph 210from a user submission.

Photograph 210 includes a representation of a fire truck 212 and atraffic sign 216 in the foreground, and a representation of a bridge 214in the background. As such, in response to a user submission includingthe photograph 210 being posted, the object identification apparatus116, for example, can identify the fire truck 212 as a first object andthe bridge 214 as a second object. For example, the objectidentification apparatus 116 can use a pixel or feature matching processto compare the pixels or features of the photograph 210 defining thefire truck 212 (a “pixel group”) to the pixels or features of aphotograph or image of a fire truck from a corpora of images with knownsubject matters (e.g., stored in an image data store accessible by theobject identification apparatus 116) to identify the fire truck 212 asan object in the photograph 210. As used herein, a reference image canbe an image or object, or image or object characteristic(s) orfeature(s), having known subject matter, to which a photograph from auser submission can be compared. In some implementations, if the numberof matched pixels or features between the reference image and the pixelgroup from the photograph (e.g., the fire truck 212) exceed a similaritythreshold value, the object identification apparatus 116 can identifythe pixel group as an object. For example, the similarity thresholdvalue can be a 90% match or 90% similarity in pixels, a threshold cosinesimilarity value based on a feature vector comparison, or any othermatch or similarity values.

The process 200 determines a subject matter of the user submission basedat least in part on the one or more objects from the user submission(202C). In some implementations, the subject matter identificationapparatus 118 can determine a subject matter of the user submissionbased at least in part on an identified object from the photograph inthe user submission (e.g., based on known images, common objects, andrecognizable concepts such as logos). For example, the subject matteridentification apparatus 118 can determine that the user submissionincluding the photograph 210 has a subject matter related to fires andfire trucks based on the identification of the fire truck 212 as anobject in the photograph 210.

The subject matter identification apparatus 118 can determine thesubject matter of a user submission in numerous ways, such as from themetadata of images similar to the photograph in the user submission,from web pages that host images similar to the photograph, from searchqueries for which search results were selected that referenced imagessimilar to the photograph, from textual content included in the usersubmission, or some combination thereof. As described above, in someimplementations, the object identification apparatus 116 can identify apixel group from a photograph in a user submission to be an object basedon a matching process to a reference image (e.g., a pixel-to-pixel orfeature-to-feature comparison process). In such implementations, eachreference image stored in the reference image data store can beassociated with one or more keywords (e.g., the metadata for eachreference image includes a keyword for the reference image).

The subject matter identification apparatus 118 can extract the keywordfrom the metadata of the reference image to which the pixel group of thephotograph is matched (or determined to be similar) and assigns thekeyword from the matched reference image to the user submission as thesubject matter of the user submission. For example, if the referenceimage to which the fire truck 212 is matched is associated with thekeyword “fire” then the subject matter identification apparatus 118 candetermine that the user submission including the photograph 210 has asubject matter of fire. Other techniques, beyond an image matchingprocess can be used to identify objects in a photograph as being similaror the same as a reference image (e.g., cross correlation, scaleinvariant feature transform, categorizations in the same subject matterverticals). Regardless of how an object in a photograph is matched ordetermined to be similar to a reference image (“matched referenceimage”), the subject matter identification apparatus 118 can determinethe subject matter of the user submission based on keywords associatedwith the matched reference image.

In some implementations, the subject matter identification apparatus 118can use the matched reference image to query the search system 112 todetermine or otherwise access keyword(s) on web pages which host thematched reference image (e.g., from the indexed cache 114). The subjectmatter identification apparatus 118 can assign these web page keywordsas the subject matter for the respective user submission. Such web pagekeywords can be the titles of the respective web pages, headings on therespective web pages, annotations or captions for the matched referenceimages on the respective web pages, etc.

In some implementations, the indexed cache 114 can store web pagekeywords for reference images hosted on the web pages. As such, thesubject matter identification apparatus 118 can request or otherwiseaccesses the relevant web page keywords from the indexed cache 114 orsearch system 112 based on the particular matched reference image. Forexample, the subject matter identification apparatus 118 can use thematched reference image for the fire truck 212 to request from thesearch system 112 the web page keywords from one or more web pages thathost the matched reference image for the fire truck 212 and assign oneor more of these web page keywords to be the subject matter of the usersubmission. As described below, the particular keywords assigned ordetermined to be the subject matter of the user submission can be basedon keyword or term frequency in the web pages.

In some implementations, the subject matter identification apparatus 118can communicate with and provide to the search system 112 a matchedreference image. In return, the search system 112 can provide one ormore search queries for which search results were selected thatreferenced (e.g., included links to) the matched reference image or,more generally, the object or pixel group in the photograph to which thematched reference image was matched.

The subject matter identification apparatus 118 can use one or more ofthe terms from the returned search queries (e.g., the search termappearing in the greatest number of returned search queries) as theterm(s) that is assigned to be the subject matter of the correspondinguser submission. For example, the subject matter identificationapparatus 118 can provide the matched reference image for the fire truck212 (e.g., a photograph of a fire truck that is the same make and modelas the fire truck 212) to the search system 112. In turn, the searchsystem 112 can parse search query logs storing data of past searchqueries and parses click logs storing data for past search query resultselections to identify the search queries for which responsive searchresults, referencing or provided links linking to the matched referenceimage for the fire truck 212, were selected. The search system 112 canreturn these search queries to the subject matter identificationapparatus 118.

The subject matter identification apparatus 118 can select one or moreterms from the returned search queries to assign to be the subjectmatter of the user submission including relevant photograph. Forexample, if the returned search queries are “firefighting equipment,”“fire,” “fire trucks” and “how is a forest fire started,” the subjectmatter identification apparatus 118 can select the search query termthat appears in the greatest number of returned search queries—“fire.”However, other selection methods can also be used such as selecting theterm with the highest frequency of use across all returned searchqueries that is not an article of grammar or a preposition.

In some implementations, the subject matter identification apparatus 118can determine the subject matter of user submissions based on textualcontent included in user submissions with textual content. Moreparticularly, the subject matter identification apparatus 118 cananalyze the textual content included in a user submission (e.g., by wordfrequency distributions, pattern recognition, tagging/annotation,information extraction, and/or other data mining techniques) todetermine the subject matter of the user submission. For example, if thetextual content included in the user submission with photograph 210 is“omg fire trucks!,” the subject matter identification apparatus 118 canassign “fire trucks” or “fire” as the subject matter of the usersubmission based on an analysis of the textual content.

In some implementations, the subject matter identification apparatus 118can analyze the textual content of numerous user submissions (e.g.,posted within a particular time period such as the last ten minutes ororiginating from the same geographic region) to facilitatedeterminations of the subject matter(s) of the user submissions. Byanalyzing the textual content of numerous user submissions beforedetermining the subject matter of any one of these particular usersubmissions, the subject matter identification apparatus 118 canidentify commonalities or semantic trends among the user submissions tofacilitate the determination of the subject matter for some or all ofthe user submissions. For example, if one hundred user submissions areposted during a five minute period and eighty-five of the usersubmissions include the term “fire” or “fire truck” then the subjectmatter identification apparatus 118 can determine with a high measure ofconfidence that the user submissions that include the term “fire” or“fire truck” have subject matters related to fires.

By analyzing a group of user submissions, the subject matteridentification apparatus 118 can determine the subject matter(s) of suchuser submissions with a higher degree of confidence than by analyzing asingle user submission in isolation. For example, if the textual contentof a first user submission is “I can see a massive fire from the balconyof my hotel, which btw has amazing views of the water” and isaccompanied by a photograph of the fire, it may be challenging todetermine the user submission is primarily directed to the fire as thetextual content also includes a reference to the hotel. However, ifdozens of other user submissions are posted within two minutes in whichthe first user submission is posted and all clearly relate to a fire,then by analyzing all of these user submissions, the subject matteridentification apparatus 118 can determine with a higher degree ofconfidence (e.g., determine as between fire and hotel) that the firstuser submission is primarily directed to a fire as all of the other usersubmissions are also directed to the fire.

More generally, the subject matter identification apparatus 118 cananalyze a group of user submissions that are related (e.g., in time,geographic origin, posted by users with a commonality, etc.). If athreshold level of user submissions in the group are determined to havecommon subject matter then the subject matter identification apparatus118 can use the common subject matter as an input to facilitate thedetermination of the subject matter for any user submissions in thegroup (or otherwise related) which have undiscerned, multiple orambiguous subject matter.

In some implementations, the subject matter identification apparatus 118can use any of the above described techniques, or any combinationthereof, to determine the subject matter of a user submission.

The process 200 determines a geographic location associated with thesubject matter of the user submission based at least in part on contentof the user submission (202D). In some implementations, the geographiclocation identification apparatus 120 can determine the geographiclocation based on content of the user submission such as objects in thephotograph of the user submission, metadata associated with the usersubmission, or both. For example, the geographic location identificationapparatus 120 can cooperate with the object and subject matteridentification apparatuses 116, 118 to identify objects that arelandmarks (or otherwise provide meaningful location information) in thephotographs and determine the geographic location based on the locationsof the landmarks. Thus, for example, the geographic locationidentification apparatus 120 can identify the bridge 214 in photograph210 as the Golden Gate Bridge near San Francisco, Calif. and, therefore,determine that the geographic location associated with the subjectmatter of the corresponding user submission (e.g., fire) is or isproximate San Francisco, Calif.

As described above, in some implementations, the geographic locationidentification apparatus 120 can determine the geographic location basedon metadata associated with the user submission. For example, themetadata can be EXIF data for the photograph specifying the location thephotograph was taken. The geographic location identification apparatus120 can determine the geographic location for the user submission to bethe same as the location specified in the EXIF data.

In some implementations, the geographic location identificationapparatus 120 can determine the geographic location based on locationinformation for the user submission itself. For example, usersubmissions can be geotagged with the location of the user device 106posting the user submission (e.g., as determined by the globalpositioning system of the user device 106). The geographic locationidentification apparatus 120 can use this geotag information todetermine the geographic location. For example, the geographic locationidentification apparatus 120 can determine that the geographic locationassociated with the subject matter of the corresponding user submissionto be the same as the location specified in the geotag information ofthe user submission.

In some implementations, the geographic location identificationapparatus 120 can determine the geographic location based on the IPaddress of the user device 106 posting the user submission. For example,the geographic location identification apparatus 120 can access locationinformation for the IP address of the user device 106 posting the usersubmission from an IP address/location lookup data store and determinethe geographic location to be the same as the location associated withthe IP address of the posting user device 106. The geographic locationidentification apparatus 120 can use any one of the above describedtechniques, or any combination thereof, to determine the geographiclocation.

As described above, the process 200 analyzes user submissions to, inpart, identify the times user submissions were posted, and determine thesubject matters of the user submissions and the geographic locationsassociated with such subject matters. If the frequency of usersubmissions having a similar subject matter and geographic locationincreases over a given time interval, it is likely that an event of someimportance related to such subject matter and geographic location hasoccurred. As such, it is desirable to provide information related tosuch an event to members of the social network. The identification ofsuch events is described below.

The process 200 determines clusters of the user submissions (204). Eachuser submission in a particular cluster is similar to each other usersubmission in the particular cluster based at least in part on the timesthe user submissions occurred, the subject matters of the usersubmissions, the geographic locations associated with the subject matterof the user submissions or some combination thereof. Thus a cluster canbe a grouping of user submissions related in at least one of time,subject matter or geographic location. For example, a cluster can becomposed of user submissions that have the same or similar subjectmatters and geographic locations that were posted with in a particularthree minute window.

User submissions can be determined to have similar or related subjectmatters, for example, if the subject matters of the user submissions areclassified in the same vertical/subject matter categories or if the usersubmissions include the same or related (e.g., semantically related)textual content (e.g., keywords) or image content (e.g., as determinedthrough image matching techniques). User submissions can be determinedto be similar or related in time, for example, if the user submissionswere submitted within a specified time period relative to each other orwithin a specified time period relative to a particular day or timeduring the day. User submissions can be determined to be similar orrelated in geographic location, for example, if the user submissionswere submitted at or within or include content associated with aparticular geographic region (e.g., the user submissions were submittedby users in California or the user submissions include photographs ofthe Golden Gate Bridge). In some implementations, the clusterdetermination apparatus 122 can determine clusters of the usersubmissions.

The cluster determination apparatus 122 can determine clusters based on,for example, various techniques such as k-means clustering, hierarchicalclustering or density-based clustering. The determination of clusters isdescribed with reference to FIG. 2C, which is an example diagram 280 ofuser submission clusters. The diagram 280 includes numerous designators281 each representing a particular user submission received during aspecified time period (e.g. one hour). The diagram 280 represents atwo-dimensional space with the y-axis representing the geographiclocation of the subject matter of a user submission and the x-axisrepresenting the subject matter of the user submission. In athree-dimensional representation of user submissions, the z-axis would,for example, represent the time the user submission was posted.

With respect to the diagram 280, the cluster determination apparatus 122determines or identifies, for example, three clusters of usersubmissions: clusters 282, 284 and 286. The user submissions in thecluster 282 are similar in both subject matter (e.g., fire) andgeographic location (e.g., San Francisco) as the user submissions in thecluster are concentrated in a relatively small region (in terms ofsubject matter and geographic location). In other words the variation ofsubject matter and geographic location for user submissions in thecluster across the x- and y-axes is within some specified range definingthe cluster.

Likewise, the user submissions in the cluster 284 are similar in bothsubject matter (e.g., the World Cup finals) and geographic location(e.g., Chicago). However, clusters can also be composed of usersubmissions related in only subject matter as the nature of thecorresponding event may be geographically agnostic or distributed. Forexample, the user submissions in the cluster 286 are geographicallydistributed (e.g., dissimilar) but similar in terms of subject matter asindicated by the low variance in subject matter (e.g., small rangeacross the x-axis) and high variance in geographic location (e.g., largerange across the y-axis). Once such example, of a cluster of usersubmissions having a similar subject matter but being geographicallydistributed are user submissions of presidential election results fromacross the country on election day.

In some implementations, the cluster determination apparatus 122 canidentify clusters that have a number of related user submissions (e.g.,related in subject matter, time, geographic location or any combinationthereof) that exceed a cluster threshold value for a cluster of suchrelated user submissions. A cluster threshold value can be a thresholdmeasure of user submissions which must be exceeded for the related usersubmissions to constitute a cluster. A cluster threshold value can bebased on, for example, the number of related user submissions or on thenumber or volume of user submissions posted over a specified interval(e.g., user submission rate). For example, for a particular cluster withuser submissions having a subject matter related to a fire in SanFrancisco, the cluster threshold value is two hundred related usersubmissions per hour. As such, the cluster determination apparatus 122can identify a group of user submissions related to a fire in SanFrancisco as a cluster in response to determining there are at least twohundred such user submissions posted during a one hour interval.

The level or value of a cluster threshold can be set to reduce thelikelihood that user submissions related to subject matter which is ofinterest to only a small fraction of social network members aredetermined to be a cluster or are used for determining which content orthe volume of such content to deliver to, for example, members of asocial network. As described below, as content for distribution acrossthe social network can be selected based on determined clusters or on aparticular group of clusters, subject matter that is of interest to onlya small fraction of the social network members is likely not a goodcandidate for distribution. Thus the cluster determination apparatus 122or a social network administrator can set a cluster threshold to a valuethat reduces the likelihood of clusters being identified or determinedbased on a limited number of user submissions. This, in turn, reducesthe likelihood of distribution of content related to such usersubmissions to the general social network audience.

As described above, the diagram 280 is based on user submissionsreceived during a specified time period. The cluster determinationapparatus 122 can vary the time window across which clusters of usersubmissions are determined. In some implementations, the clusterdetermination apparatus 122 can vary the time window based on the typeof subject matter. For example, some subject matter is associated withevents that occur over a relatively brief time period (e.g., emergencyevents, such as a building fire or earthquake) while other subjectmatter is associated with events that occur over a relatively long timeperiod (e.g., gradual seasonal events, such as fall foliage).

Thus for subject matter associated with events that occur over a shorttime period, it is expected that the intensity of relevant usersubmissions (e.g., the number of submissions per unit time) posted willbe relatively high. Accordingly, for such subject matters, to minimizethe likelihood that the cluster determination apparatus 122 will misssuch events/groups of user submission, in some implementations, thecluster determination apparatus 122 can analyze the user submissions forsuch clusters at frequent intervals. Such frequent looking consumes moresystem resources than looking for clusters at less frequent intervals.

Likewise, for subject matter associated with events that occur over along time period, it is expected that the intensity of relevant usersubmissions posted will be lower than that of user submissions relatedto events that occur over a short time period. Accordingly, for suchlonger occurring events, in some implementations, the clusterdetermination apparatus 122 can analyze the user submissions forcorresponding clusters at intervals less frequent than that for clustersof user submissions related to events that occur over a short timeperiod. This, in turn, reduces the burden on system resources ascompared to the frequent intervals for clusters of user submissionsrelated to events that occur over a short time period.

Given the relative user submission intensity levels described above forsubject matters associated with events that occur over short or longtime periods, in some implementations, the cluster determinationapparatus 122 can set a cluster threshold value based on the expectedintensity levels. The expected intensity levels can be, for example,based on historical measures. As such, the cluster determinationapparatus 122 can set the cluster threshold values for clusters of usersubmissions related to events that occur over a short time period at anintensity level higher than those for clusters of user submissionsrelated to events that occur over a long time period. For example, asuser submissions having a subject matter associated with fall foliageevents will likely be posted at relatively low intensity levels giventhe gradual foliage change in any one geographic area and the differenttiming of fall foliage events in different geographic areas, the clusterdetermination apparatus 122 will set the cluster threshold value at alower level than the cluster threshold value for a cluster of usersubmissions related to an event that occurs over a short time period andfor which a high intensity level of user submissions is expected.

In some implementations, the cluster determination apparatus 122 canalso vary the frequency with which it looks for clusters of usersubmissions with a particular subject matter based on historicaloccurrences of user submissions with that particular subject matter. Forexample, the cluster determination apparatus 122 can access a databaseof past user submissions and determine when particular clusters wereidentified in the past. Thus if the cluster determination apparatus 122identified a cluster associated with fall foliage last October then thecluster determination apparatus 122 can start looking for a cluster offall foliage-related user submissions this October with a frequencygreater than it did, for example, in June given the previousidentification of the cluster last October.

Given that a determined cluster is likely a good indicator of aninterest of a set of social network members, the cluster determinationapparatus 122 can use the determined clusters to adjust or selectcontent to distribute across the social network to social networkmembers as described below.

In some implementations, the cluster determination apparatus 122analyzes (or further analyzes) the user submissions to identify clustersof “unusual” events. An unusual event is an event that deviates (e.g.,by a specified threshold deviation) from a baseline or norm for acategory of events in which the event of interest is categorized. Anevent or the user submission about the event is categorized in one ormore particular categories based on similarities between the subjectmatter of the event and the subject matter of the particular category orcategories. For example, for a category of user submissions about firetruck related events, a majority of the user submissions include aphotograph of a fire truck at a fire station (e.g., the norm or baselinefor the category). Thus a user submission that includes a photograph ofa fire truck at a bridge is a rarer occurrence than a user submissionthat includes a photograph of a fire truck at a fire station (e.g., itdeviates from the norm of user submissions that include photographs offire trucks). Such rarity or deviation from a norm can signify anunusual or abnormal event.

In some implementations, the cluster determination apparatus 122identifies clusters of unusual user submissions (e.g., user submissionsthat include unusual or abnormal events). For example, treating the usersubmissions as a time series, the cluster determination apparatus 122can use various statistical techniques such as, for example, a leastsquares analysis to identify abnormal or unusual user submissions. Thusthe cluster determination apparatus 122 can analyze a group of usersubmissions submitted during a particular time frame (e.g., a timeseries of user submissions) and, for example, based on a statisticalanalysis of the user submissions, identify clusters of unusual usersubmissions. For example, a cluster of unusual user submissionsincluding photographs of a fire truck at a bridge can be identified.

The process 200 adjusts the delivery of content to members of the secondnetwork based on the determination of one or more of the clusters (206).For example, if a determined cluster relates to a fire in San Franciscothen the cluster determination apparatus 122 can generate a headlinetitled “Fire in San Francisco” and distributes the content across thesocial network. The headline can, for example, include a link linking toan album or an aggregation of user submissions from the cluster. Inanother example, the cluster determination apparatus 122 can select anews feed about the fire and distribute the news feed or show the fireas a trending topic. As the clusters can be determined in a real time ornear real time process, the cluster determination apparatus 122 candistribute or provide content relevant to the subject matter of thedetermined cluster in a timely manner with respect to the occurrence ofthe associated event.

More generally, the content delivery adjustment system 110 can adjustthe delivery of content based on the user submission rate for a clusterof user submissions. For example, the content delivery adjustment system110 can increase a delivery volume of content (e.g., the number ofcontent items delivered or the rate which the content items aredelivered) having subject matter similar to that of the subject matterof the user submissions in the clusters that exceed their respectivecluster threshold values (e.g., the clusters determined from the process204).

In some implementations, the cluster determination apparatus 122 can,for example, provide the determined clusters to the search system 112and the search system 112 can use the subject matter associated with thedetermined clusters to provide semantic context for search queries toincrease the relevancy of search results returned in response to thesearch queries. For example, if a slightly ambiguous search query isreceived, the search system 112 can use the subject matter associatedwith the received clusters (and the context that such subject matter iscurrently trending) to resolve or help resolve the ambiguity in thesearch query.

Although the above description has focused on user submissions withphotographs, the methods and processes described herein are equallyapplicable to user submissions that include audio clips, audio/videoclips, drawings and the like. For example, the content adjustmentdelivery system 110 can use various audio analysis techniques todetermine the subject matter of an audio clip and assign the extractedsubject matter to be the subject matter of the corresponding usersubmission. Likewise, the content adjustment delivery system 110 can usevarious image and audio analysis techniques to determine the subjectmatter of an audio/video clip and assign the extracted subject matter tobe the subject matter of the corresponding user submission.

Embodiments of the subject matter and the operations described in thisspecification can be implemented in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Embodiments of the subject matterdescribed in this specification can be implemented as one or morecomputer programs, i.e., one or more modules of computer programinstructions, encoded on computer storage medium for execution by, or tocontrol the operation of, data processing apparatus. Alternatively or inaddition, the program instructions can be encoded on an artificiallygenerated propagated signal, e.g., a machine-generated electrical,optical, or electromagnetic signal, that is generated to encodeinformation for transmission to suitable receiver apparatus forexecution by a data processing apparatus. A computer storage medium canbe, or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate physical components or media (e.g., multiple CDs, disks, orother storage devices).

The operations described in this specification can be implemented asoperations performed by a data processing apparatus on data stored onone or more computer-readable storage devices or received from othersources.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing. The apparatus can includespecial purpose logic circuitry, e.g., an FPGA (field programmable gatearray) or an ASIC (application specific integrated circuit). Theapparatus can also include, in addition to hardware, code that createsan execution environment for the computer program in question, e.g.,code that constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more of them.The apparatus and execution environment can realize various differentcomputing model infrastructures, such as web services, distributedcomputing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astandalone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. Processors suitable for the execution of a computerprogram include, by way of example, both general and special purposemicroprocessors, and any one or more processors of any kind of digitalcomputer. Generally, a processor will receive instructions and data froma read only memory or a random access memory or both. The essentialelements of a computer are a processor for performing actions inaccordance with instructions and one or more memory devices for storinginstructions and data. Generally, a computer will also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, e.g., magnetic,magneto optical disks, or optical disks. However, a computer need nothave such devices. Devices suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto optical disks; andCD ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), an inter-network (e.g., the Internet), andpeer-to-peer networks (e.g., ad hoc peer-to-peer networks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits data (e.g., an HTML page) to a clientdevice (e.g., for purposes of displaying data to and receiving userinput from a user interacting with the client device). Data generated atthe client device (e.g., a result of the user interaction) can bereceived from the client device at the server.

An example of one such type of computer is shown in FIG. 3, which showsa block diagram of a programmable processing system (system). The system300 that can be utilized to implement the systems and methods describedherein. The architecture of the system 300 can, for example, be used toimplement a computer client, a computer server, or some other computerdevice.

The system 300 includes a processor 310, a memory 320, a storage device330, and an input/output device 340. Each of the components 310, 320,330, and 340 can, for example, be interconnected using a system bus 350.The processor 310 is capable of processing instructions for executionwithin the system 300. In one implementation, the processor 310 is asingle-threaded processor. In another implementation, the processor 310is a multi-threaded processor. The processor 310 is capable ofprocessing instructions stored in the memory 320 or on the storagedevice 330.

The memory 320 stores information within the system 300. In oneimplementation, the memory 320 is a computer-readable medium. In oneimplementation, the memory 320 is a volatile memory unit. In anotherimplementation, the memory 320 is a non-volatile memory unit.

The storage device 330 is capable of providing mass storage for thesystem 300. In one implementation, the storage device 330 is acomputer-readable medium. In various different implementations, thestorage device 330 can, for example, include a hard disk device, anoptical disk device, or some other large capacity storage device.

The input/output device 340 provides input/output operations for thesystem 300. In one implementation, the input/output device 340 caninclude one or more of a network interface device, e.g., an Ethernetcard, a serial communication device, e.g., and RS-232 port, and/or awireless interface device, e.g., an 802.11 card. In anotherimplementation, the input/output device can include driver devicesconfigured to receive input data and send output data to otherinput/output devices, e.g., keyboard, printer and display devices 360.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyimplementations or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments of particularimplementations. Certain features that are described in thisspecification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular embodiments of the subject matter have been described.Other embodiments are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

What is claimed is:
 1. A computer-implemented method, comprising:analyzing user posts to an online social network, wherein each of theuser posts is from a member of the social network and comprises aphotograph, the analyzing comprising: for each of the user posts:identifying a time the user post was posted; identifying one or moreobjects represented in the photograph from the user post based oncontent of the user post; determining a subject matter of the user postbased at least in part on a subject matter of the one or more objectsidentified from the user post; determining a geographic locationassociated with the user post based at least in part on a geographiclocation associated with the one or more objects; determining, by one ormore processors, clusters of the user posts, wherein each user post in aparticular cluster is similar to each other user post in the particularcluster based at least in part on the times the user posts were posted,the subject matters of the user posts, and the geographic locationsassociated with the user posts; and determining which content todelivery to members of the social network based on the subject mattersof the user posts from one or more of the clusters.
 2. Acomputer-implemented method, comprising: analyzing user submissions to anetwork, wherein each of the user submissions comprise a photograph, theanalyzing comprising: for each of the user submissions: identifying atime the user submission occurred; identifying one or more objectsrepresented in the photograph from the user submission; determining asubject matter of the user submission based at least in part on the oneor more objects identified from the user submission; determining ageographic location associated with the subject matter of the usersubmission based at least in part on content of the user submission;determining, by one or more processors, clusters of the usersubmissions, wherein each user submission in a particular cluster issimilar to each other user submission in the particular cluster based atleast in part on the times the user submissions occurred, the subjectmatters of the user submissions, and the geographic locations associatedwith the subject matter of the user submissions; and adjusting deliveryof content to members of the network based on the determination of oneor more of the clusters.
 3. The method of claim 2, wherein the usersubmissions are user posts to the network.
 4. The method of claim 2,wherein: determining a subject matter of the user submission comprises:identifying search queries for which responsive search results wereselected that referenced the one or more objects; and identifying one ormore terms from the search queries as the subject matter of the usersubmission.
 5. The method of claim 2, further comprising: identifyingclusters that have a number of user submissions that exceed a respectivecluster threshold value; and wherein adjusting delivery of contentcomprises increasing a delivery volume of content to the network forcontent having a subject matter similar to that of the subject matter ofthe user submissions in the clusters that exceed their respectivecluster threshold values.
 6. The method of claim 5, wherein each of thecluster threshold values is based on the subject matter of usersubmissions in the respective cluster.
 7. The method of claim 5, whereineach of the cluster threshold values is based on a volume of usersubmissions in the respective cluster and a time period during whichthose user submissions occurred.
 8. The method of claim 2, whereindetermining a geographic location associated with the subject matter ofthe user submission comprises determining the geographic location basedat least in part on a geotag for the user submission.
 9. The method ofclaim 2, wherein determining a geographic location associated with thesubject matter of the user submission comprises determining thegeographic location based at least in part on geographic information forthe one or more objects.
 10. The method of claim 2, wherein adjustingdelivery of content comprises providing content including a headlinewith a link linking to an aggregation of user submissions from one ormore of the determined clusters.
 11. A system comprising: one or moredata processors; and instructions stored on a computer readable storagemedium that when executed by the one or more data processors cause theone or more data processors to perform operations comprising: analyzinguser submissions to a network, wherein each of the user submissionscomprise a photograph, the analyzing comprising: for each of the usersubmissions: identifying a time the user submission occurred;identifying one or more objects represented in the photograph from theuser submission; determining a subject matter of the user submissionbased at least in part on the one or more objects identified from theuser submission; determining a geographic location associated with thesubject matter of the user submission based at least in part on contentof the user submission; determining clusters of the user submissions,wherein each user submission in a particular cluster is similar to eachother user submission in the particular cluster based at least in parton the times the user submissions occurred, the subject matters of theuser submissions, and the geographic locations associated with thesubject matter of the user submissions; and adjusting delivery ofcontent to members of the network based on the determination of one ormore of the clusters.
 12. The system of claim 11, wherein the usersubmissions are user posts to the network.
 13. The system of claim 11,wherein: determining a subject matter of the user submission comprises:identifying search queries for which responsive search results wereselected that referenced the one or more objects; and identifying one ormore terms from the search queries as the subject matter of the usersubmission.
 14. The system of claim 11, further comprising: identifyingclusters that have a number of user submissions that exceed a respectivecluster threshold value; and wherein adjusting delivery of contentcomprises increasing a delivery volume of content to the network forcontent having a subject matter similar to that of the subject matter ofthe user submissions in the clusters that exceed their respectivecluster threshold values.
 15. The system of claim 14, wherein each ofthe cluster threshold values is based on the subject matter of usersubmissions in the respective cluster.
 16. The system of claim 14,wherein each of the cluster threshold values is based on a volume ofuser submissions in the respective cluster and a time period duringwhich those user submissions occurred.
 17. The system of claim 11,wherein determining a geographic location associated with the subjectmatter of the user submission comprises determining the geographiclocation based at least in part on a geotag for the user submission. 18.The system of claim 11, wherein determining a geographic locationassociated with the subject matter of the user submission comprisesdetermining the geographic location based at least in part on geographicinformation for the one or more objects.
 19. The system of claim 11,wherein adjusting delivery of content comprises providing contentincluding a headline with a link linking to an aggregation of usersubmissions from one or more of the determined clusters.
 20. The systemof claim 11, wherein the network is an online social network.